The present invention relates in general to controlling electrical systems in vehicles, and, more specifically, to use of a time-series predictor to improve energy management of an electrical system.
Systems for generating, storing, conditioning, and using electric power in motor vehicles are becoming increasingly complex. New electrical functions, the increased use of power assist systems such as electric power-assist steering, new types of power generation systems such as integrated starter-alternator systems, fuel cells, dual battery systems, and 42-volt systems, require increasingly sophisticated control systems. Furthermore, the limited power capacity on a vehicle makes it desirable to be able to prioritize power delivery to various loads when electrical demand approaches or exceeds available supply.
Electrical energy, power, and load management systems, referred to herein as energy management (EM) systems, have been developed for coordinating the action of electrical system components to balance generated power with power consumption, protect components from harmful electrical conditions, and to utilize electrical capacity according to safety and other considerations. Due to the large number of interacting electrical components competing for capacity and each having various kinds of influences on electrical system performance, energy management strategies have become more extensive and complex. Consequently, it becomes necessary to develop expert systems, which can recognize performance issues using a minimum amount of sensed data on functional behavior of the electrical system. However, the complex electrical systems in modern vehicles are characterized by high nonlinearity (e.g., stability of power flow, battery and alternator performance and interaction, and load current profiles) which are difficult to model.
In order to provide superior performance, an EM strategy should recognize potential malfunctions in advance in order to reconfigure the electrical system to avoid such malfunction. However, advance prediction of the electrical system state is even more resource intensive and complicated. Thus, a system for predicting the state of the electrical system based on current conditions and using reduced processing resources would be desirable.